Subscription based travel service

ABSTRACT

Systems and methods are disclosed for providing a subscription travel service. The systems and methods include operations for receiving travel information with a travel date for a user; computing a subscription value as a function of a booking date and the travel date; determining a minimum travel value and a maximum purchase amount based on the computed subscription value; searching a list of travel services that are available on the travel date to identify candidate travel services each having a first cost that exceeds the minimum travel value; selecting a subset of the candidate travel services that each have a second cost that is less than the maximum purchase amount; and generating for display to a user, in a graphical user interface, one or more interactive visual representations of the selected subset of the candidate travel services.

BACKGROUND

Web-based travel services systems allow a user to search through varioustravel services available by multiple providers. A user can specify adestination and travel time frame to find matching hotels, rental cars,and airfares along with their corresponding costs. The user can sort theresults by price, type and availability of the travel service. After theuser finds a suitable hotel, rental car, or airfare, the user canutilize the web-based travel services to reserve the hotel, rental car,or airfare.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating a networked system for asubscription based travel service, according to some exampleembodiments.

FIG. 2 illustrates a travel services system, according to some exampleembodiments.

FIGS. 3-4 illustrate flow diagrams of processes of the travel servicessystem, according to some example embodiments.

FIG. 5 is an illustrative graphical user interface of the travelservices system, according to some example embodiments.

FIG. 6 is a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 7 illustrates a diagrammatic representation of a machine, in theform of a computer system, within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments. It will be evident, however, to those skilled in the art,that embodiments may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Users typically spend a great deal of time and effort making travelarrangements, such as looking for hotels, airfares, and car rentals thatare within their budget. Existing travel sites allow a user to inputvarious travel criteria, such as travel dates and destinations, tosearch for all the available hotels, airfares, and car rentals thatmatch the travel criteria. The existing travel sites also allow the userto sort and filter results based on a specified cost. While suchexisting travel sites generally work well for making travelarrangements, there is often a large disparity among cost andavailability for the same or similar hotels, airfares, and car rentalsacross different travel sites. This ends up burdening the user as theuser has to search through the available options across multiple travelsites to make sure he or she gets the best price within their budget.Searching through multiple travel sites takes a great deal of time andeffort and forces the user to navigate through multiple pages ofinformation and manually compare results to make travel arrangements.Even still, the travel arrangements the user finally settles on may notprovide the user with the best available options for the budget.

The disclosed embodiments improve the efficiency of using an electronicdevice to improve and provide a better way for users to consume travel,such as by making travel arrangements using a subscription-based travelservice. The subscription-based travel service, according to thedisclosed embodiments, allows a user to search for travel services andmake reservations for travel services (e.g., such as hotels, rentalcars, airfares, homes/residences, experiential travel, guided tours,cruises, train fares, private aviation, “glamping”, bespoke travel,event-based travel, and/or space travel) for a fixed annual or monthlysubscription fee. Specifically, the user can pay a monthly or annualsubscription fee and make an unlimited number of reservations for travelservices without having to consider budgetary constraints. Namely, thetravel service automatically identifies, curates, and generates apredetermined list of all of the best available travel service optionsfor a specified travel period and destination from which the user canselect based on the user's subscription value as a function of thebooking start date (e.g., the date on which the user views and selectsto reserve a given travel service) and the travel date (e.g., the dateon which the selected travel service begins, such as the first night atthe hotel). In this way, the amount of time and effort the user has tospend searching for travel services that meet the user's budget aresignificantly reduced. Also, by providing a single interface and travelsite for making travel arrangements, that automatically take intoaccount various travel service costs in providing travel servicesoptions to the user, the number of steps, pages, and interfaces the userhas to navigate through to make travel arrangements are reduced. Thisprovides a better way for a user to consume travel. Namely, the userdoes not need to search through multiple travel sites and pages ofinformation to find travel arrangements that satisfy the user's needs.

To automatically provide the user with the list of available travelservices, the disclosed embodiments receive travel information (e.g.,automatically, before the user requests to view a curated list of travelservice options or in response to the user specifying the travelinformation) with a travel date and, in response, compute a subscriptionvalue as a function of a booking date and the travel date. In somecases, the subscription value includes an accumulated value portion,representing the total amount the user will have paid for thesubscription from the booking date to the travel date, and an amortizedvalue portion, representing an estimate of the total amount of thesubscription the user will have paid from the booking date to a giventime period, such as a week relative to the annual subscription cost. Avalue guard is generated to search for travel services using thesubscription value. Specifically, the value guard is generated bycomputing a minimum travel value and a maximum purchase amount based onthe computed subscription value. A list of travel services that areavailable on the travel date is searched to identify candidate travelservices that each has a first cost (e.g., a cost available through apublicly available database or travel site) that exceeds the minimumtravel value. Then, a subset of the candidate travel services that eachhave a second cost (e.g., a cost available exclusively to subscribers ofthe travel service via the subscription travel services system database)that is less than the maximum purchase amount is selected and generatedfor display to the user in a graphical user interface using one or moreinteractive visual representations. The user can select a given one ofthe displayed visual representations to instruct the system toautomatically reserve the travel service associated with the selectedvisual representation. In some embodiments, the subset of the candidatetravel services is filtered based on a classification of the user asdetermined by a trained machine learning technique.

FIG. 1 is a block diagram illustrating a networked system 100 for asubscription based travel service, according to some exampleembodiments. The system 100 includes one or more client devices such asclient device 110. The client device 110 comprises, but is not limitedto, a mobile phone, desktop computer, laptop, portable digitalassistants (PDA), smart phone, tablet, ultrabook, netbook, laptop,multi-processor system, microprocessor-based or programmable consumerelectronic, game console, set-top box, computer in a vehicle, or anyother communication device that a user may utilize to access thenetworked system 100. In some embodiments, the client device 110comprises a display module to display information (e.g., in the form ofgraphical user interfaces). In further embodiments, the client device110 comprises one or more of touch screens, accelerometers, gyroscopes,cameras, microphones, global positioning system (GPS) devices, and soforth. The client device 110 may be a device of a user that is used toaccess and utilize subscription based travel services via a travelservices system 124 implemented by an application server 102.

For example, the client device 110 may be used by a user to navigate toa website of the travel services system 124. In some embodiments, theclient device 110 may include a dedicated travel services system 124application with the same or similar functionality as the website. Afteraccessing the website, the user inputs personal information (e.g., name,address, phone number, payment information, and so forth) to subscribeto the travel services system 124. In some embodiments, the subscriptionfee is paid monthly but can be paid on any other periodic interval(e.g., weeks, daily, every other month, annual, lifetime, and so forth).After subscribing to the travel services system 124, the user isprovided with login credentials that can be used to navigate and browseavailable travel services on the travel services system 124. Forexample, the user can access the travel services system 124 to browsehotel rooms available in various luxury categories in a selectedgeographical location at a particular date or range of dates.

In some embodiments, the client device 110 presents a graphical userinterface with data entry regions allowing the user to select from apredefined list of travel destinations (e.g., geographical locations).In some embodiments, the graphical user interface allows the user tomanually type in a name of a desired geographical location. As the usertypes in the name of the desired geographical location, the travelservices system 124 searches through available travel destinations andpresents the available travel destinations to the user for selection.

In response to receiving a user selection of one or more of the traveldestinations, prior to or during selection of the destination, theclient device 110 presents a data entry region for the user to input aspecific travel start date (e.g., an arrival date at the hotel) and anumber of days for the trip. In some embodiments, the list of availabletravel services are automatically searched for on a daily basis withoutreceiving the user selection of the travel destination and/or travelstart date. The travel services system 124 retrieves subscriptioninformation for the user specifying the amount the user pays on amonthly or other periodic basis. Using the subscription information, thetravel services system 124 computes a subscription value as a functionof the booking date and the travel date. The booking date may becomputed based on the current date on which the user selection of thetravel destination is received and/or the current date on which a listof travel services is searched and curated. The travel services system124 utilizes the subscription value and a value guard to search fortravel services that satisfy the subscription value and the value guard.The value guard is used as a filter of travel services to ensure thatthe travel service options presented to the user have a cost and/orvalue that satisfies a minimum travel value amount and does not exceed amaximum purchase amount. The travel services system 124 providesmatching results to the client device 110 for presentation of theresults in the graphical user interface using one or more interactivevisual representations. The graphical user interface of the clientdevice 110 may be utilized to access reviews, comments, and additionalinformation for each of the travel services represented by theinteractive visual representations.

The client device 110 receives a user input selecting one of theinteractive visual representations for a travel service and communicatesthe selection to the travel services system 124. The travel servicessystem 124 automatically reserves the travel service (e.g., holds andpays for a room at a hotel) corresponding to the selected interactivevisual representation. The client device 110 may present a confirmationpage to the user informing the user of the travel service that has beenreserved and the travel start date.

In some implementations, the travel services system 124 may limit thenumber of concurrent travel services the user can reserve. For example,the travel services system 124 may allow the user to select only onetravel service reservation at a time, such that the user is preventedfrom searching for and/or reserving additional travel services until thecurrently selected travel service that has been reserved expires or iscanceled. As another example, the travel services system 124 may onlyallow the user to reserve three travel services at a time, such thatwhen one of the three travel services expires, the user can reserve anadditional travel service. Namely, after the start and end dates for thetravel service elapse indicating that the user has utilized the travelservice, the client device 110 may allow the user to search foradditional travel services to reserve in a similar manner as before.Alternatively, the user can navigate to a cancellation page or graphicaluser interface using the client device 110 and cancel any reservationspreviously selected within a cancellation window (e.g., within 72 hoursprior to the travel start date). In response to receiving a user requestto cancel the travel service, the travel service system 124 may cancelthe reservation and the client device 110 may allow the user to searchfor a new travel service in a similar manner as before.

In some embodiments, the travel services system 124 provides an improvedand better way for users to consume travel. The travel services system124 performs such improved techniques in three phases or steps.Initially, in the first phase or step, the travel services system 124generates an inventory of travel services by searching traveldestinations across a range of date or dates throughout the year. Thetravel destinations are searched from publicly available informationsources (e.g., databases of other travel sites available tonon-subscribers of the travel services system 124), by direct access toa predetermined set of travel services, third party sources, proprietarysources, and travel services that have direct relationships andcontracts for travel services with the travel services system 124. Thetravel destinations are searched periodically (e.g., nightly or weekly)and using various combinations of travel dates and destinations. Thesearch returns travel services available at various dates throughout theworld and includes the total cost for consuming the travel services onthe particular combination of dates along with the cancellation policyof each travel service. The cancellation policy may indicate the fee forcanceling the travel service once booked which may be free or a nominalcharge. As a result, the output of phase one or step one is a collectionor database of tens of millions of combinations of travel services (andtravel service types), at different ranges of travel start dates, withcorresponding prices or costs, and with corresponding cancellationpolicies.

After the first phase or step, the travel services system 124 performs asecond step or phase. In the second step or phase, the collection of thetravel services identified in the first phase is curated or filtered inaccordance with one or more rules. Specifically, any, all or acombination of the information associated with each travel service(e.g., the travel start dates, the prices, the travel service type, thedestination, and the cancellation policy) is analyzed and compared withthe one or more rules to exclude and selected a list of candidate travelservices. In an embodiment, the rules include three criteria (e.g., thebooking date or date on which the reservation for a given travel serviceis made or requested, the price with taxes and fees (cost of thereservation), and the cancellation fee or policy) which are used tocurate or filter the collection of travel services. The rules may varybetween users or may be uniform for all users and subscribers of thetravel services system 124.

Specifically, the rules consider how much the travel services system 124is willing or allowed (e.g., the maximum purchase amount) to spend for agiven travel service which is leveraged against how far in advance thereservation is being made (e.g., the difference between the booking dateand the travel start date). The maximum purchase amount may be computedbased on two factors of payments received (e.g., the amount a subscriberwill actually end up paying from the booking date to the travel date andan amortized amount by week of the subscriber's subscription cost).Specifically, the amount the subscriber will actually end up paying maybe computed by determining how many subscription cycles or how manypayments will be collected between the booking date and the travel startdate. For example, the subscriber pays monthly on the first day of everymonth and the booking date is in the middle of a given month and thetravel start date is 2 months from the booking date. In such cases, thesubscriber will end up paying 2 cycles of subscription fees—two monthlypayments—by the time the trip starts. The amortized amount is lessgranular and represents on a repeated time interval (e.g., daily,monthly, weekly, hourly) basis how much the subscriber would end uppaying. The maximum purchase amount is then offset by a margin which maybe positive or negative. The margin may vary based on how far in advancethe reservation is being made (e.g., the difference between the bookingdate and the travel start date). The margin may vary based on the typeof travel service being booked or reserved. For example, the margin maybe greater for travel services that include or relate to cruises andsmaller margin for travel services that include or relate tohomes/residences.

The travel services system 124 computes a minimum travel valuerepresenting the maximum a given user would be willing to pay for thetravel service. This may be computed as a percentage (e.g., 80%) of theamount the subscriber would have paid by the time the trip begins.Specifically, the amount is a percentage of the number of subscriptioncycle payments the subscriber would have made by the travel start datestarting from the booking date. This amount is used to remove any travelservices that have a cost that is less than the minimum travel value asthe subscriber can shop those travel services independently of being asubscriber to the travel services system 124. The travel services system124 eliminates any duplicates from the travel services and maintainsthose travel services that have a maximum duration of travel dates. Forexample, if the travel services system 124 identifies the same hotelhaving 2, 3 and 5 night stay options in the same time period, the travelservices system 124 selects only the 5 night option and removes orfilters out the 2 and 3 night stay options during the same time period.

The travel services system 124 searches the actual price or cost of thevarious travel services and apply a margin to the cost of each travelservice. The margin may be positive or negative and may depend on howfar in advance the travel date is relative to the booking date. Thetravel services system 124 filters any travel service that has a costthat exceeds the maximum purchase amount and filters any travel servicethat has a cost that is below the minimum travel value. The travelservices system 124 applies an additional filter based on cancellationpolicies of travel services that do not satisfy a given cancellationpolicy criteria.

In some embodiments, the travel services system 124 presents thefiltered list of travel services as options for the user or subscriberto select to make a reservation. The user can further filter the listbased on various criteria (e.g., travel dates, travel destinations,etc.). In some embodiments, the travel services system 124 presents to auser a comparison of each travel service that is presented against whatis available for the same travel service on a publicly available orother travel site. Specifically, the travel services system 124 presentsnext to each travel service or next to a portion of travel services anidentification of another booking travel site that has the same travelservice and the cost for booking that same travel service on the anotherbooking travel site. This cost that is presented for comparison may beretrieved from storage based on what is in the collection that isanalyzed and filtered to generate the list and/or may be determinedautomatically by accessing the another travel site, executing a searchfor the particular travel service and the particular range of traveldates, and retrieving the cost presented on the another travel sitebased on the executed search.

One or more users may be a person, a machine, or other means ofinteracting with the client device 110. In example embodiments, the usermay not be part of the system 100 but may interact with the system 100via the client device 110 or other means. For instance, the user mayprovide input (e.g., touch screen input or alphanumeric input) to theclient device 110 and the input may be communicated to other entities inthe system 100 (e.g., third-party servers 130, server system 108, etc.)via a network 104. In this instance, the other entities in the system100, in response to receiving the input from the user, may communicateinformation to the client device 110 via the network 104 to be presentedto the user. In this way, the user interacts with the various entitiesin the system 100 using the client device 110.

The system 100 further includes a network 104. One or more portions ofnetwork 104 may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe public switched telephone network (PSTN), a cellular telephonenetwork, a wireless network, a WiFi network, a WiMax network, anothertype of network, or a combination of two or more such networks.

The client device 110 may access the various data and applicationsprovided by other entities in the system 100 via web client 112 (e.g., abrowser, such as the Internet Explorer® browser developed by Microsoft®Corporation of Redmond, Wash. State) or one or more client applications114. The client device 110 may include one or more client applications114 (also referred to as “apps”) such as, but not limited to, a webbrowser, messaging application, electronic mail (email) application, ane-commerce site application, a mapping or location application, anonline home buying and selling application, a travel servicesapplication, a real estate application, and the like.

In some embodiments, one or more client applications 114 are included ina given one of the client device 110, and configured to locally providethe user interface and at least some of the functionalities, with theclient application 114 configured to communicate with other entities inthe system 100 (e.g., third-party servers 130, server system 108, etc.),on an as-needed basis, for data and/or processing capabilities notlocally available (e.g., to access location information, to accesstravel services information, such as cost and availability, toauthenticate a user, to verify a method of payment, etc.). Conversely,one or more applications 114 may not be included in the client device110, and then the client device 110 may use its web browser to accessthe one or more applications hosted on other entities in the system 100(e.g., third-party servers 130, server system 108, etc.).

A server system 108 provides server-side functionality via the network104 (e.g., the Internet or WAN) to one or more third-party servers 130and/or one or more client devices 110. The server system 108 includes anapplication server 102 that implements an application program interface(API) server 120, a web server 122, and a travel services system 124,that may be communicatively coupled with one or more databases 128. Theone or more databases 128 may be storage devices that store data relatedto users of the system 108, applications associated with the system 108,cloud services, travel services data, one or more machine learningtechniques and so forth. The one or more databases 128 may further storeinformation related to third-party servers 130, third-party applications132, client devices 110, client applications 114, users, and so forth.In one example, the one or more databases 128 may be cloud-basedstorage.

In one example, the one or more databases 128 may be cloud-basedstorage. The one or more databases 128 may store subscriptioninformation for one or more users of the travel services system 124. Thesubscription information may identify users of the travel servicessystem 124, the subscription start dates of the users, the subscriptionfee of the users, the total amount paid to date for a subscription ofthe users, and one or more travel services activities of the users. Thetravel services activities may include any combination of the number ofreservations made in a given time period (e.g., within a givensubscription year) by each user, the subscription duration (e.g.,measured from the subscription start date to the present date) of eachuser, the booking duration (e.g., measured from the booking date to thetravel date) of each user, the distance to the travel destination ofeach user (e.g., measured from an address of the user and the locationof reserved travel destinations), the margin amount (e.g., how muchprofit was made in aggregate during the course of the subscription) foreach user, the cancellation frequency (e.g., how often the user cancelsa reservation made), and/or the reservation frequency (e.g., how muchtime elapses on average between the end of one reservation and the startof another).

The one or more databases 128 may store the reservations (e.g., thedestination and the travel start date and/or duration) of travelservices of each user or subscriber of the travel services system 124.The one or more databases 128 may store a list of all the available, ora selected set, of travel services in one or more geographical regionsor destinations along with reviews and/or detailed information about thetravel services. The one or more databases 128 may store first andsecond costs on a nightly basis or on some other periodic interval(e.g., per 6 night basis) for each travel service. The first cost thatis stored in the one or more databases 128 may represent the cost forthe travel service that is provided to non-subscribers of the travelservices system 124 and that is available by directly making thereservation through a dedicated server of the travel service and/or bymaking the reservation through an existing travel service searchinterface. The one or more databases 128 may access a dedicated existingtravel service search interface on a periodic basis (e.g., nightly orweekly) to obtain and download the first cost of each, or a selectedset, of travel services. The first cost may be computed by selecting aspecified travel duration (e.g., 6 nights) and multiplying the per nightrate (provided by the travel service) by the specified travel duration.The second cost of each travel service may be a dedicated cost that ischanged on an annual or monthly basis and is provided by contractbetween the travel services system 124 and the corresponding travelservice. The second cost may only be available to users who subscribe tothe travel services system 124. The second cost of each travel servicemay represent the cost for consuming the travel service during aspecified travel duration (e.g., 6 nights).

The one or more databases 128 may store the cancellation policy of eachtravel service indicating how much time in advance of the reservationstart date at a given travel service the travel service reservation canbe canceled without penalty (e.g., to receive a full refund). The one ormore databases 128 may store the cost for canceling a given travelservice outside of the cancellation policy. The one or more databases128 may store an expected margin on a per user basis. The expectedmargin may increase over time (e.g., for subscribers classified as veryactive) or decrease over time (e.g., for subscribers classified as notvery active). The expected margin may change by a predetermined factorbased on a difference between a booking date and a travel start date(e.g., the margin may change based on how far in advance a user ismaking the reservation). This may be used to reduce the maximum purchaseamount by a first factor if the reservation is made less than apredetermined number of days in advance of the travel date. This may beused to increase the maximum purchase amount by a second factor if thereservation is made more than a predetermined number of days in advanceof the travel date.

The server system 108 may be a cloud computing environment, according tosome example embodiments. The server system 108, and any serversassociated with the server system 108, may be associated with acloud-based application, in one example embodiment.

The server system 108 includes a travel services system 124. The travelservices system 124 includes one or more modules, storage devices, anddatabases. The storage devices in the travel services system 124 storevarious travel services activities for each user, travel servicesactivities training data, and one or more machine learning techniquesfor classifying users of the travel services system 124. The modules intravel services system 124 are configured to compute components of asubscription value, compute value guards, and search for availabletravel services to provide to the client device 110 in response toreceiving a request for travel services at a given destination and timeframe. The modules in travel services system 124 are configured toreceive a user selection of one of the travel services matching therequest and reserve the selected travel service for the user. Themodules in travel services system 124 are configured to determinewhether the number of pending reservations for a given user exceed anallowable number of pending reservations (e.g., more than one, or morethan three) and in response prevent the user from making furtherreservations until the number of pending reservations is below theallowable number (e.g., by canceling a pending reservation or waitingfor the reservation to expire).

The modules in travel services system 124 are configured to train amachine learning technique to classify a given user or subscriber usingthe travel services activities of the user or subscriber by establishingrelationships between known travel services activities and known ormanually assigned classifications to users or subscribers. The modulesin travel services system 124 are configured to filter the availabletravel services provided to a given client device 110 based on theclassification of the user of the client device 110 and/or cancellationpolicies of the various travel services. The details of the travelservices system 124 are provided below in connection with FIG. 2.

The system 100 further includes one or more third-party servers 130. Theone or more third-party servers 130 may include one or more third-partyapplication(s) 132. The one or more third-party application(s) 132,executing on third-party server(s) 130, may interact with the serversystem 108 via API server 120 via a programmatic interface provided bythe API server 120. For example, one or more the third-partyapplications 132 may request and utilize information from the serversystem 108 via the API server 120 to support one or more features orfunctions on a website hosted by the third party or an applicationhosted by the third party. The third-party website or application 132,for example, may provide software version analysis functionality that issupported by relevant functionality and data in the server system 108.

Third-party servers 130 may include an existing non-subscription basedtravel service. Such non-subscription based travel services can be usedto search for travel services at a first cost available tonon-subscribers of the travel services system 124. The travel servicessystem 124 may query the third-party servers 130 on a periodic basis toobtain the first costs for the travel services provided by the travelservices system 124. The first costs may represent a per-night rate ofthe travel services multiplied by a predetermined number of nights(e.g., 6 nights).

FIG. 2 illustrates a travel services system 124, according to someexample embodiments. The travel services system 124 includes a travelservices training data module 210, a machine learning technique trainingmodule 220, a trained machine learning technique module 230, a newtravel service request module 240, a subscription value module 250, atravel services search module 260, and a travel value guard module 252.In some implementations, some modules of the travel services system 124may be implemented on server system 108 and others may be implemented onthird party servers 130 or client device 110. In some implementations,all of the modules of the travel services system 124 are implemented onserver system 108, on third party servers 130, or on client device 110.In such cases, server system 108 communicates information to third partyservers 130 based on the modules implemented and vice versa.

The new travel service request module 240 may communicate with theclient device 110 to receive parameters and criteria for a new travelservice request. For example, via the graphical user interface of theclient device 110, the user can select a travel destination orgeographical location and can, optionally, input the desired trip startdate, end date, and/or trip length. The new travel service requestmodule 240 may communicate this user selection to the travel servicessearch module 260 to identify a list of available travel services. Thenew travel service request module 240 may communicate an identifier ofthe user of the client device 110 to the subscription value module 250.In some embodiments, the parameters are automatically determined andcomputed on a nightly basis and used to curate a list of travel servicesover the course of a given user. In such cases, the user may enter atravel destination and the curated list is presented with previouslyselected travel dates (e.g., travel dates not inputted or selected bythe user). In such cases, the new travel service request module 240 may,on a periodic basis (e.g., nightly) retrieve a subscription value forone or more users and one or more travel destinations and provide theseas the selection to the travel services search module 260. In this way,the travel services search module 260 identifies available travelservices across a range of dates for one or more users and curates sucha list for subsequent presentation to the user. In this way, the usercan simply enter a desired destination and the available and curatedlist of travel services at the destination, together with the availabletravel dates, are presented to the user.

The travel services search module 260 may communicate with thesubscription value module 250 to obtain the subscription value for theuser of the client device 110. The subscription value module 250 maycommunicate with the databases 128 to obtain the booking date and thesubscription cost of the identified user. The booking date may be thecurrent date indicating when the search module 260 conducts the searchfor available travel services and/or the date on which the user requestto view available travel services is received from the new travelservice request module 240. The subscription value module 250 maycompute the subscription value based on two parameters: an aggregatedsubscription cost parameter and an amortized subscription costparameter. In an example, the subscription value module 250 computes thesubscription value as an average of the aggregated and the amortizedsubscription cost parameters.

For example, based on the data provided by the user, the subscriptionvalue module 250 may determine that the trip is scheduled to start 10weeks from the present time. In such cases, the subscription valuemodule 250 computes an estimate of the total amount the user will payfor the subscription by aggregating the total amount that will be paidfrom the present time until 10 weeks from the present time. Namely, thesubscription value module 250 assumes the user will continue paying forthe subscription until the travel start date from the booking date andestimates how much the user would have paid for the subscription fromthe current booking date until the future travel start date. As anexample, if the subscription costs $2500 per month, the subscriptionvalue module 250 may determine that the trip will start 10 weeks fromthe present day and, in the next 10 weeks, three months' worth ofsubscription fees (e.g., $7500) will be paid (assuming the fee is paidon the first day of every month). Accordingly, the subscription valuemodule 250 may compute as the aggregated subscription cost parameter ofthe subscription value $7500, that will be paid from present time (thebooking date) until the trip start time.

The subscription value module 250 may also compute as the subscriptionvalue an amortized amount of the subscription cost over an annual basis.For example, the subscription value module 250 may determine the totalcost of the subscription for the entire year $30,000 (e.g., bymultiplying the number of months in a year, 12, by the monthlysubscription fee, $2500). The subscription value module 250 may amortizethe yearly subscription cost on a specified repeated period (e.g.,daily, monthly, hourly, weekly) basis to determine the amount of thesubscription paid from the booking date until the travel start date. Forexample, if the trip is planned to start in 10 weeks, the subscriptionvalue module 250 computes, as the amortized subscription cost parameterof the subscription value, a total of 10 weeks' worth of the weeklysubscription cost as $5,769 (e.g., annual subscription fee $30,000divided by 52 weeks per year and multiplied by 10 weeks).

The subscription value module 250 may compute the subscription value asa function of the aggregate subscription cost expected to have been paidby the time the trip starts and the amortized subscription cost by thetime the trip starts as measured from the booking date. For example, ifthe user plans the trip to start in 10 weeks from today (the bookingdate), the subscription value module 250 computes an average of $7,500and $5,769. As another example, the subscription value module 250computes the subscription value as a weighted average or sum of theaggregated and amortized subscription cost parameters.

The subscription value module 250 provides the parameters of thesubscription value to the travel value guard module 252. The travelvalue guard module 252 is configured to compute a guard range having aminimum travel value and a maximum purchase amount based on thesubscription value. The guard range ensures that the travel servicesthat are identified by the travel services search module 260 satisfyminimum parameters that ensure a subscriber receives a better deal orbargain than making the same reservation for the travel service throughanother travel service system (e.g., a travel service system provided bythe third-party servers 130). The guard range also ensures that thetravel services that are identified by the travel services search module260 satisfy a margin amount that provides a positive or negative levelof profitability to the travel services system 124. The margin amountmay be computed based on a difference between the booking date and thetravel date, such that the margin is greater when the difference issmaller than a threshold and is lower when the difference is greaterthan a threshold. Namely, the minimum travel value is used to ensurethat travel service results provided to the user have a value, asdetermined by the first cost associated with the travel services, thatis greater than the minimum travel value. Also, the maximum purchaseamount is used to ensure that the travel service results provided to theuser are not valued, as determined by the second cost associated withthe travel services, greater than the maximum purchase amount. In somecases, the first and second costs may be the same values and in othercases they are different values.

As an example, the travel value guard module 252 computes the minimumtravel value as a function of the aggregated (or accumulated)subscription cost parameter of the subscription value. Specifically, thetravel value guard module 252 computes the minimum travel value as 80percent of the aggregated (or accumulated) subscription cost parameter.Accordingly, if the aggregated subscription cost is determined to be$7,500, the minimum travel value is computed to be $6,000 (e.g., 80percent of $7,500).

As an example, the travel value guard module 252 computes the maximumpurchase amount as a function of an adjusted average of the aggregatedand amortized subscription cost parameters. The average may be adjustedbased on a margin amount or value that is associated with the user thatis retrieved by the travel value guard module 252 from the databases128. Specifically, the travel value guard module 252 computes themaximum purchase amount as an average of the aggregated (or accumulated)subscription cost parameter and the amortized subscription costparameter offset by the retrieved margin. Accordingly, if the aggregatedsubscription cost is determined to be $7,500, the amortized subscriptioncost parameter is $5,769, and the margin is −2.5%, the maximum purchaseamount value is computed to be $6,468 (e.g., (average of $7,500 and$5,769)*-2.5%)). In some implementations, the margin may change based ona cancellation policy of a given travel service and/or a cancellationfrequency of a given user. In some implementations, the margin maychange based on the travel date specified by the travel search requestand/or based on a duration of time between a current date (the bookingdate) and the start date of the travel request.

The travel services search module 260 receives the guard range from thetravel value guard module 252 and searches for travel services that fallwithin the guard range and that satisfy the travel criteria (optionally)supplied by the user received from the new travel service request module240. As an example, the travel services search module 260 first searchesfor all of the travel services that are available on the travel daterange (e.g., the travel start date and the travel duration) receivedfrom the client device 110 and/or received automatically by the travelservice request module 240. The travel services search module 260restricts or limits the search to those travel services that are withina specified range (e.g., 25 miles) of the travel destination orgeographical region received from the client device 110 and/or receivedautomatically by the travel service request module 240. In some cases,the travel services search module 260 access a predefined list of traveldestinations and searches all of the available travel services availablein 6-day periods (or other defined periods) during the course of theentire year. The travel services search module 260 searches variouscombinations of travel dates and destinations to generate millions ofcombinations of possible travel destinations at various periods.

After travel services search module 260 identifies the list of travelservices that are available on the travel start date and that meet thetravel destination or geographical region parameters, the travelservices search module 260 obtains first and second costs associatedwith each of the travel destinations from the databases 128. The travelservices search module 260 compares the first or second costs of each ofthe identified travel services to the minimum travel value received fromthe travel value guard module 252. The travel services search module 260removes or filters from the list any travel service that has a first orsecond cost that is below the minimum travel value. The travel servicessearch module 260 may also filter out and remove any travel destinationthat has a cancellation policy that fails to satisfy cancellation policycriteria. The travel services search module 260 removes or filters fromthe list any travel service that has a second cost that is above themaximum purchase amount received from the travel value guard module 252.To determine the first or second cost, the travel services search module260 may multiply a nightly first and/or second cost of each travelservice during the travel period by the number of days in the travelservice request. In some cases, the travel services search module 260communicates with the trained machine learning technique module 230 toobtain a classification for the user making the travel request andfurther filters or removes travel services based on the classificationof the user. The travel services search module 260 provides the filteredlist of travel services back to the new travel service request module240 for provision to the client device 110 and presentation to the userfor selection and requesting to make a reservation.

In some cases, the travel services search module 260 may searchdatabases of travel services with second costs that are only availablefor reservation by the travel services system 124 and/or subscribers ofthe travel services system 124. This search may initially identify allof the travel services that have a cost that does not exceed the maximumpurchase amount. Then, the travel services search module 260 may accesspublicly available databases of travel sites available tonon-subscribers for the same identified travel services. The travelservices search module 260 may compute a cost for the same identifiedtravel services if purchased or reserved using the travel sitesavailable to non-subscribers. This cost may be the first cost. Thetravel services search module 260 may determine whether these computedcosts are below the minimum travel value, and if so, such travelservices are removed and filtered from the list that is then provided tothe user or subscriber of the travel services system 124.

To classify users, the trained machine learning technique module 230 isinitially trained based on training data. Specifically, the travelservices training data module 210 includes a list of travel servicesactivities associated with various subscribers of the travel servicessystem 124. The travel services activities are obtained by the travelservices training data module 210 from database 128 and/or from thirdparty server 130. For example, the travel services training data module210 obtains the number of reservations made by a user from database 128and obtains the cancellation frequency from third party server 130. Thetravel services training data module 210 may access training dataincluding the number of reservations made by each user, the subscriptionduration of each user, the distance to travel destination of each user,the margin amount of each user, the reservation frequency of each user,the cancellation frequency of each user, and an assigned classificationof each user. The classification may represent a level of activity ofeach user from not active, to medium active, to very active. Theclassification is used to control and filter the types and quantity oftravel services provided to different users. This can be used as ameasure to ensure that users who are not very active are provided agreater quantity of a better type of travel services than a very activeuser to incentivize the non-active user to utilize the travel servicessystem 124.

Machine learning technique training module 220 is trained to predict aclassification for a subscriber of the travel services system 124 byestablishing a relationship between one or more known travel servicesactivities of other users provided by travel services training datamodule 210 and the corresponding known classification of the other usersprovided by the travel services training data module 210.

Machine learning is a field of study that gives computers the ability tolearn without being explicitly programmed. Machine learning explores thestudy and construction of algorithms, also referred to herein as tools,that may learn from existing data and make predictions about new data.Such machine-learning tools operate by building a model from exampletraining data (e.g., travel services activity information) in order tomake data-driven predictions or decisions expressed as outputs orassessments. Although example embodiments are presented with respect toa few machine-learning tools, the principles presented herein may beapplied to other machine-learning tools. In some example embodiments,different machine-learning tools may be used. For example, LogisticRegression (LR), Naive-Bayes, Random Forest (RF), neural networks (NN),matrix factorization, and Support Vector Machines (SVM) tools may beused for classifying a given user based on travel activities of theuser.

The machine-learning algorithms utilize features (e.g., variouscombinations of travel services activities performed by other users ininteracting and making reservations with the travel services system 124)for analyzing the data to generate assessments (e.g., a classificationof the users). A feature is an individual measurable property of aphenomenon being observed. The concept of a feature is related to thatof an explanatory variable used in statistical techniques such as linearregression. Choosing informative, discriminating, and independentfeatures is important for effective operation of in pattern recognition,classification, and regression. Features may be of different types, suchas numeric features, strings, and graphs.

In one example embodiment, the features may be of different types andmay include one or more of a number of reservations made by each user,the subscription duration of each user, the distance to traveldestination of each user, the margin amount of each user, thereservation frequency of each user, and the cancellation frequency ofeach user.

The machine-learning algorithms utilize the training data to findcorrelations among the identified features that affect the outcome orassessment (e.g., the known or assigned classification of each user). Insome example embodiments, the training data includes labeled data, whichis known data for one or more identified features and one or moreoutcomes, such as the assigned classification of the user.

Once the training data are collected and processed, machine learningtechnique training module 220 model can be built using eitherstatistical learning or machine learning techniques. In one embodiment,regression analysis can be used to build the machine learning techniquetraining module 220 model. Regression analysis is a statistical processfor estimating the relationships among variables. There are a number ofknown methods to perform regression analysis. Linear regression orordinary least squares regression, among others, are “parametric” inthat the regression function is defined in terms of a finite number ofunknown model parameters that can be estimated from training data. Fordays to pending prediction, a regression model (e.g., Equation 1) can bedefined, for example, as:

H≈f(X,β),  (Equation 1)

where “H” denotes the known days to pending amount for a set ofproperties, “X” denotes a vector of input variables (e.g., any one ofthe travel services activities associated with the set of users), and“P” denotes a vector of unknown parameters to be determined or trainedfor the regression model.

The training data that include travel services activities of varioususers and the corresponding classification (which can be manually orautomatically specified for each user) provide a set of known H values(e.g., the classification of a user) having corresponding X values(e.g., feature vectors extracted from the travel services activities).Using these data, the model parameter β can be computed using datafitting techniques such as least squares, maximum likelihood, or thelike. Once β is estimated, the model can then compute H (e.g., a usertravel services classification) for a new set of X values (e.g., featurevectors extracted from a new set of travel services activities).

Machine learning techniques train models to accurately make predictionson data fed into the models (e.g., what was said by a user in a givenutterance; whether a noun is a person, place, or thing; what the weatherwill be like tomorrow). During a learning phase, the models aredeveloped against a training dataset of inputs to optimize the models tocorrectly predict the output for a given input. Generally, the learningphase may be supervised, semi-supervised, or unsupervised, indicating adecreasing level to which the “correct” outputs are provided incorrespondence to the training inputs. In a supervised learning phase,all of the outputs are provided to the model and the model is directedto develop a general rule or algorithm that maps the input to theoutput. In contrast, in an unsupervised learning phase, the desiredoutput is not provided for the inputs so that the model may develop itsown rules to discover relationships within the training dataset. In asemi-supervised learning phase, an incompletely labeled training set isprovided, with some of the outputs known and some unknown for thetraining dataset.

Models may be run against a training dataset for several epochs (e.g.,iterations), in which the training dataset is repeatedly fed into themodel to refine its results. For example, in a supervised learningphase, a model is developed to predict the output for a given set ofinputs and is evaluated over several epochs to more reliably provide theoutput that is specified as corresponding to the given input for thegreatest number of inputs for the training dataset. In another example,for an unsupervised learning phase, a model is developed to cluster thedataset into n groups and is evaluated over several epochs as to howconsistently it places a given input into a given group and how reliablyit produces the n desired clusters across each epoch.

Once an epoch is run, the models are evaluated and the values of theirvariables are adjusted to attempt to better refine the model in aniterative fashion. In various aspects, the evaluations are biasedagainst false negatives, biased against false positives, or evenlybiased with respect to the overall accuracy of the model. The values maybe adjusted in several ways depending on the machine learning techniqueused. For example, in a genetic or evolutionary algorithm, the valuesfor the models that are most successful in predicting the desiredoutputs are used to develop values for models to use during thesubsequent epoch, which may include random variation/mutation to provideadditional data points. One of ordinary skill in the art will befamiliar with several other machine learning algorithms that may beapplied with the present disclosure, including linear regression, randomforests, decision tree learning, neural networks, deep neural networks,and so forth.

Each model develops a rule or algorithm over several epochs by varyingthe values of one or more variables affecting the inputs to more closelymap to a desired result, but as the training dataset may be varied, andis preferably very large, perfect accuracy and precision may not beachievable. A number of epochs that make up a learning phase, therefore,may be set as a given number of trials or a fixed time/computing budget,or may be terminated before that number/budget is reached when theaccuracy of a given model is high enough or low enough or an accuracyplateau has been reached. For example, if the training phase is designedto run n epochs and produce a model with at least 95% accuracy, and sucha model is produced before the n^(th) epoch, the learning phase may endearly and use the produced model satisfying the end-goal accuracythreshold. Similarly, if a given model is inaccurate enough to satisfy arandom chance threshold (e.g., the model is only 55% accurate indetermining true/false outputs for given inputs), the learning phase forthat model may be terminated early, although other models in thelearning phase may continue training. Similarly, when a given modelcontinues to provide similar accuracy or vacillate in its results acrossmultiple epochs—having reached a performance plateau—the learning phasefor the given model may terminate before the epoch number/computingbudget is reached.

Once the learning phase is complete, the models are finalized. In someexample embodiments, models that are finalized are evaluated againsttesting criteria. In a first example, a testing dataset that includesknown outputs for its inputs is fed into the finalized models todetermine an accuracy of the model in handling data on which it is hasnot been trained. In a second example, a false positive rate or falsenegative rate may be used to evaluate the models after finalization. Ina third example, a delineation between data clusterings is used toselect a model that produces the clearest bounds for its clusters ofdata. In some embodiments, the machine learning technique trainingmodule 220 is trained to establish a relationship to classify a userbased on a logistic regression of one or more features (e.g., trainingdata received from travel services training data module 210).

After being trained, the machine learning technique is provided totrained machine learning technique module 230. In one example, thecoefficient values of the machine learning technique (e.g., the linearmodel) are stored in a storage of trained machine learning techniquemodule 230. Trained machine learning technique module 230 is configuredto receive new travel services activities of a new user from new travelservice request module 240. For example, the new travel service requestmodule 240 receives a user input that identifies a desired traveldestination and travel dates and accesses previously stored interactioninformation for the user (e.g., the number of prior reservations made bythe user and the distance traveled by the user from the user's homeaddress to the travel destinations). The new travel service requestmodule 240 accesses database 128 and/or server 130 to obtain the travelservices activities for the new user. For example, new travel servicerequest module 240 obtains the number of reservations previously made bythe user, the subscription duration of the user, the distance traveledby the user to the destinations, the margin amount stored for the user,the reservation frequency of the user, and/or the cancellation frequencyof the user. The new travel service request module 240 instructs thetrained machine learning technique module 230 to apply the trainedmachine learning technique using the previously computed coefficients tothe data provided by the new travel service request module 240. Trainedmachine learning technique module 230 provides a classification for thenew user based on the data provided by the new travel service requestmodule 240.

In one example, after being trained, the machine learning techniquetraining module 220 classifies a new user as a very active user as afunction of the number of reservations made by the user in a givensubscription period (e.g., within a year). A user that makes more thantwo reservations in a given month may be classified as a very activeuser and be provided more limited and less interesting travel servicesoptions for a given search request than another user who is classifiedas a not very active user and provides the same search request. Forexample, if a first user classified as very active searches for a hotelin Miami during December 24 for 6 nights, a first set of hotels thatincludes 5 hotels that are rated 4 stars may be presented to the user asoptions to make a reservation. A second user classified as not veryactive who, concurrently with the first user, searches for a hotel inMiami during December 24 for 6 nights, may be presented with a secondset of hotels that includes the 5 hotels in the first set and 10 morehotels that are rated 5 stars as options to select to make areservation.

FIGS. 3-4 illustrate flow diagrams of processes of the travel servicessystem 124, according to some example embodiments. The processes 300-400may be embodied in computer-readable instructions for execution by oneor more processors such that the operations of the processes 300-400 maybe performed in part or in whole by the functional components of theserver system 108; accordingly, the processes 300-400 are describedbelow by way of example with reference thereto. However, in otherembodiments at least some of the operations of the processes 300-400 maybe deployed on various other hardware configurations. The processes300-400 are therefore not intended to be limited to the server system108 and can be implemented in whole, or in part, by any other component.Any operation in the processes 300-400 can be performed in any order orentirely omitted and skipped.

At operation 301, a computing system (e.g., server system 108) receivestravel service information representing different types of travelactivities performed by users of the travel service. For example, travelservices training data module 210 obtains from database 128 and/orserver 130 travel services activities of various types associated withusers of the travel services system 124 (e.g., number of reservationsmade, subscription duration, distance to travel destinations, marginamount, reservation frequency, cancellation frequency, etc.).

At operation 302, the computing system determines, for each of theusers, a user classification, based on the different types of travelactivities. For example, travel services training data module 210accesses data stored in database 128 that indicates the knownclassification of each user for which travel activities information isreceived.

At operation 303, the computing system trains a machine learningtechnique to establish a relationship between the different types oftravel activities and the determined user classification. For example,travel services training data module 210 provides the known travelactivities of the users and the known classifications associated witheach user to machine learning technique training module 220. Machinelearning technique training module 220 inputs the received data into alinear model (e.g., a log odds model) to estimate or computecoefficients associated with each activity. In some implementations,machine learning technique training module 220 performs a regressiontechnique to estimate the coefficients of the model.

At operation 304, the computing system applies the trained machinelearning technique to travel activities associated with a new user toassociate a classification with the new user. For example, new travelservice request module 240 obtains a travel request from a user, via agraphical user interface on a client device 110, and obtains fromdatabase 128 travel activities previously performed by the user. Thetrained machine learning technique module 230 is applied to theinformation provided by the new travel service request module 240 toobtain and associate a classification with the new user and/or newtravel request.

At operation 305, the computing system filters a candidate list oftravel services presented to the new user based on the classificationassociated with the new user. For example, the travel services searchmodule 260 receives the user classification and filters identifiedtravel services that match a travel search request based on the userclassification.

At operation 401, the computing system receives travel information witha travel date for a user. This travel information may be received from auser or may be automatically selected as a range of date or datesthrough a given year. For example, the new travel service request module240 communicates with a client device 110 to obtain travel criteria(e.g., travel destination and travel dates) from a user.

At operation 402, the computing system computes a subscription value asa function of a booking date and the travel date. For example, thesubscription value module 250 computes the subscription value parameters(e.g., aggregated and amortized subscription cost parameters) based onsubscription information stored in database 128 for the user.

At operation 403, the computing system determines a minimum travel valueand a maximum purchase amount based on the computed subscription value.For example, the travel value guard module 252 determines a guard rangefor searching travel services based on adjusted aggregated and amortizedsubscription cost parameters (e.g., by applying a margin offset value toan average of the aggregated and amortized subscription costparameters).

At operation 404, the computing system searches a list of travelservices that are available on the travel date to identify candidatetravel services each having a first cost that exceeds the minimum travelvalue. For example, the travel services search module 260 searchestravel services that satisfy the travel criteria input by the clientdevice 110 and that have first cost values that exceed the minimumtravel value of the guard range provided by the travel value guardmodule 252.

At operation 405, the computing system selects a subset of the candidatetravel services that each have a second cost that is less than themaximum purchase amount. For example, the travel services search module260 filters travel services that satisfy the travel criteria input bythe client device 110 to remove travel services that have second costvalues that exceed the maximum purchase value of the guard rangeprovided by the travel value guard module 252.

At operation 406, the computing system generates for display, in agraphical user interface interactive, one or more visual representationsof the selected subset of the candidate travel services. For example,the travel services search module 260 provides the list of searchresults that satisfy the travel criteria and the guard range (andoptionally are filtered based on the user classification provided by thetrained machine learning technique module 230) to the new travel servicerequest module 240. The new travel service request module 240 presentsthe search results back to the user at the client device 110 in agraphical user interface.

FIG. 5 is an illustrative graphical user interface of the travelservices system 124, according to some example embodiments. As shown, auser can input travel search criteria 501. This travel criteria mayinclude various parameters 502 including a travel destination, distanceto the destination, start date of the travel, end date of the travel,number of days in the trip, quality of the travel services, and/or anycombination thereof. The travel services system 124 processes the travelsearch criteria and automatically generates a list of matching travelservices for presentation using one or more interactive visualrepresentations 503. In some cases, the travel services system 124processes the travel search criteria and automatically selects one of aplurality of previously generated and curated lists of travel servicesfor presentation using one or more interactive visual representations503. A user can select any one of the interactive visual representations503 to instruct the travel services system 124 to complete a reservationfor the corresponding travel service associated with the selected visualrepresentation.

In some embodiments, the travel services in the graphical user interfaceof FIG. 5 are generated using individualized travel service lists forthe user based on travel behaviors, geographical location, demographics,or a margin target for the user. For example, the travel services system124 may further filter or re-organize the list of available travelservices presented to the user in FIG. 5 based on a profile of a givenuser that indicates various attributes of the user (e.g., travelbehaviors, geographical location, demographics, cancellation frequency,number of reservations made in a given time interval, or a margin targetspecific to the user or classification of the user). The graphical userinterface of FIG. 5 may include an option enabling the user to sort orfilter the travel services presented in the graphical user interfacebased on a predicted likelihood that the user will reserve the travelservices using a preference technique or a recommendation technique. Insuch cases, the travel services system 124 obtains a profile of the userand applies a recommendation algorithm to predict which travel serviceshave a greater likelihood of being selected for reservation by the userthan others. Those that have a greater likelihood may then be presentedat the top of the list as the user is more likely to be interested toreserve those travel services and those with lower likelihood arepresented at the bottom of the list (or omitted entirely). Namely, if atravel service is associated with a likelihood of being booked by theuser that is less than a threshold, such a travel service may beexcluded from the list presented to the user in FIG. 5.

FIG. 6 is a block diagram illustrating software architecture 606, whichcan be installed on any one or more of the devices described above. Forexample, in various embodiments, client devices 110 and servers andsystems 130, 108, 120, 122, and 124 may be implemented using some or allof the elements of software architecture 606. FIG. 6 is merely anon-limiting example of a software architecture, and it will beappreciated that many other architectures can be implemented tofacilitate the functionality described herein. In various embodiments,the software architecture 606 is implemented by hardware (including ahardware layer 652 with processing unit 654, memory/storage 656, andother hardware 658) such as machine 700 of FIG. 7 that includesprocessors 704, memory/storage 706, and input/output (I/O) components718. As explained below, the processing unit 654 is configured toexecute instructions 604 that are stored in memory/storage 656. In thisexample, the software architecture 606 can be conceptualized as a stackof layers where each layer may provide a particular functionality. Forexample, the software architecture 606 includes layers such as anoperating system 602, libraries 620, frameworks 618, and applications616. Operationally, the applications 616 invoke API calls 608 throughthe software stack and receive messages 612 in response to the API calls608, consistent with some embodiments.

In various implementations, the operating system 602 manages hardwareresources and provides common services. The operating system 602includes, for example, a kernel 622, services 624, and drivers 626. Thekernel 622 acts as an abstraction layer between the hardware and theother software layers, consistent with some embodiments. For example,the kernel 622 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 624 can provide other commonservices for the other software layers. The drivers 626 are responsiblefor controlling or interfacing with the underlying hardware, accordingto some embodiments. For instance, the drivers 626 can include displaydrivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers,flash memory drivers, serial communication drivers (e.g., UniversalSerial Bus (USB) drivers), WI-FI® drivers, audio drivers, powermanagement drivers, and so forth.

In some embodiments, the libraries 620 provide a low-level commoninfrastructure utilized by the applications 616. The libraries 620 caninclude system libraries 644 (e.g., C standard library) that can providefunctions such as memory allocation functions, string manipulationfunctions, mathematic functions, and the like. In addition, thelibraries 620 can include API libraries 646 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as Moving Picture Experts Group-4 (MPEG4), AdvancedVideo Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3),Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec,Joint Photographic Experts Group (JPEG or JPG), or Portable NetworkGraphics (PNG)), graphics libraries (e.g., an OpenGL framework used torender in two dimensions (2D) and in three dimensions (3D) graphiccontent on a display), database libraries (e.g., SQLite to providevarious relational database functions), web libraries (e.g., WebKit toprovide web browsing functionality), and the like. The libraries 620 canalso include a wide variety of other libraries 648 to provide many otherAPIs to the applications 616.

The frameworks 618 provide a high-level common infrastructure that canbe utilized by the applications 616, according to some embodiments. Forexample, the frameworks 618 provide various graphic user interfacefunctions, high-level resource management, high-level location services,and so forth. The frameworks 618 can provide a broad spectrum of otherAPIs that can be utilized by the applications 616, some of which may bespecific to a particular operating system 602 or platform.

In an example embodiment, the applications 616 include built-inapplications 638 including any one or more of a home application, acontacts application, a browser application, a book reader application,a location application, a media application, a messaging application, agame application, and a broad assortment of other applications such as athird-party application 640. According to some embodiments, theapplications 616 are programs that execute functions defined in theprograms. Various programming languages can be employed to create one ormore of the applications 616, structured in a variety of manners, suchas object-oriented programming languages (e.g., Objective-C, Java, orC++) or procedural programming languages (e.g., C or assembly language).In a specific example, the third-party application 666 (e.g., anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform)may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. Inthis example, the third-party application 640 can invoke the API calls608 provided by the operating system 602 to facilitate functionalitydescribed herein.

Some embodiments may particularly include a subscription based travelservices application. In certain embodiments, this may be a stand-aloneapplication that operates to manage communications with a server systemsuch as third-party servers 130 or server system 108. In otherembodiments, this functionality may be integrated with anotherapplication. The subscription based travel services application mayrequest and display various data related to subscription based travelservices and may provide the capability for a user to input data relatedto the objects via a touch interface, keyboard, or using a camera deviceof machine 700, communication with a server system via I/O components718, and receipt and storage of object data in memory/storage 706.Presentation of information and user inputs associated with theinformation may be managed by subscription based travel servicesapplication using different frameworks 618, library 620 elements, oroperating system 602 elements operating on a machine 700.

FIG. 7 is a block diagram illustrating components of a machine 700,according to some embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 7 shows a diagrammatic representation of the machine700 in the example form of a computer system, within which instructions710 (e.g., software, a program, an application 616, an applet, an app,or other executable code) for causing the machine 700 to perform any oneor more of the methodologies discussed herein can be executed. Inalternative embodiments, the machine 700 operates as a standalone deviceor can be coupled (e.g., networked) to other machines. In a networkeddeployment, the machine 700 may operate in the capacity of a servermachine 130, 108, 120, 122, 124, etc., or a client device 110 in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 700 cancomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a PDA, an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smart watch), a smarthome device (e.g., a smart appliance), other smart devices, a webappliance, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 710, sequentially orotherwise, that specify actions to be taken by the machine 700. Further,while only a single machine 700 is illustrated, the term “machine” shallalso be taken to include a collection of machines 700 that individuallyor jointly execute the instructions 710 to perform any one or more ofthe methodologies discussed herein.

In various embodiments, the machine 700 comprises processors 704, memory714, and I/O components 718, which can be configured to communicate witheach other via a bus 702. In an example embodiment, the processors 704(e.g., a central processing unit (CPU), a reduced instruction setcomputing (RISC) processor, a complex instruction set computing (CISC)processor, a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), another processor, or anysuitable combination thereof) include, for example, a processor 708 anda processor 712 that may execute the instructions 710. The term“processor” is intended to include multi-core processors 704 that maycomprise two or more independent processors 704 (also referred to as“cores”) that can execute instructions 710 contemporaneously. AlthoughFIG. 7 shows multiple processors 704, the machine 700 may include asingle processor 704 with a single core, a single processor 704 withmultiple cores (e.g., a multi-core processor 704), multiple processors704 with a single core, multiple processors 704 with multiples cores, orany combination thereof.

The memory/storage 706 comprises a main memory 714, a static memory, anda storage unit 716 accessible to the processors 704 via the bus 702,according to some embodiments. The storage unit 716 can include amachine-readable medium on which are stored the instructions 710embodying any one or more of the methodologies or functions describedherein. The instructions 710 can also reside, completely or at leastpartially, within the main memory 714, within the static memory, withinat least one of the processors 704 (e.g., within the processor's cachememory), or any suitable combination thereof, during execution thereofby the machine 700. Accordingly, in various embodiments, the main memory714, the static memory, and the processors 704 are consideredmachine-readable media.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium is shown, in an example embodiment, to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 710. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing instructions (e.g., instructions 710) for executionby a machine (e.g., machine 700), such that the instructions 710, whenexecuted by one or more processors of the machine 700 (e.g., processors704), cause the machine 700 to perform any one or more of themethodologies described herein. Accordingly, a “machine-readable medium”refers to a single storage apparatus or device, as well as “cloud-based”storage systems or storage networks that include multiple storageapparatus or devices. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, one or more datarepositories in the form of a solid-state memory (e.g., flash memory),an optical medium, a magnetic medium, other non-volatile memory (e.g.,erasable programmable read-only memory (EPROM)), or any suitablecombination thereof. The term “machine-readable medium” specificallyexcludes non-statutory signals per se.

The I/O components 718 include a wide variety of components to receiveinput, provide output, produce output, transmit information, exchangeinformation, capture measurements, and so on. In general, it will beappreciated that the I/O components 718 can include many othercomponents that are not shown in FIG. 7. The I/O components 718 aregrouped according to functionality merely for simplifying the followingdiscussion, and the grouping is in no way limiting. In various exampleembodiments, the I/O components 718 include output components 726 andinput components 728. The output components 726 include visualcomponents (e.g., a display such as a plasma display panel (PDP), alight emitting diode (LED) display, a liquid crystal display (LCD), aprojector, or a cathode ray tube (CRT)), acoustic components (e.g.,speakers), haptic components (e.g., a vibratory motor), other signalgenerators, and so forth. The input components 728 include alphanumericinput components (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and force of touches or touch gestures, orother tactile input components), audio input components (e.g., amicrophone), and the like.

In some further example embodiments, the I/O components 718 includebiometric components 730, motion components 734, environmentalcomponents 736, or position components 738, among a wide array of othercomponents. For example, the biometric components 730 include componentsto detect expressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram based identification), and the like. The motioncomponents 734 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components736 include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensor components (e.g., machine olfactiondetection sensors, gas detection sensors to detect concentrations ofhazardous gases for safety or to measure pollutants in the atmosphere),or other components that may provide indications, measurements, orsignals corresponding to a surrounding physical environment. Theposition components 738 include location sensor components (e.g., a GPSreceiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication can be implemented using a wide variety of technologies.The I/O components 718 may include communication components 740 operableto couple the machine 700 to a network 732 or devices 720 via a coupling724 and a coupling 722, respectively. For example, the communicationcomponents 740 include a network interface component or another suitabledevice to interface with the network 732. In further examples,communication components 740 include wired communication components,wireless communication components, cellular communication components,near field communication (NFC) components, BLUETOOTH® components (e.g.,BLUETOOTH® Low Energy), WI-FI® components, and other communicationcomponents to provide communication via other modalities. The devices720 may be another machine 700 or any of a wide variety of peripheraldevices (e.g., a peripheral device coupled via a USB).

Moreover, in some embodiments, the communication components 740 detectidentifiers or include components operable to detect identifiers. Forexample, the communication components 740 include radio frequencyidentification (RFID) tag reader components, NFC smart tag detectioncomponents, optical reader components (e.g., an optical sensor to detecta one-dimensional bar codes such as a Universal Product Code (UPC) barcode, multi-dimensional bar codes such as a Quick Response (QR) code,Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code,Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes,and other optical codes), acoustic detection components (e.g.,microphones to identify tagged audio signals), or any suitablecombination thereof. In addition, a variety of information can bederived via the communication components 740, such as location viaInternet Protocol (IP) geo-location, location via WI-FI® signaltriangulation, location via detecting a BLUETOOTH® or NFC beacon signalthat may indicate a particular location, and so forth.

In various example embodiments, one or more portions of the network 732can be an ad hoc network, an intranet, an extranet, a VPN, a LAN, aWLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, aportion of the PSTN, a plain old telephone service (POTS) network, acellular telephone network, a wireless network, a WI-FI® network,another type of network, or a combination of two or more such networks.For example, the network 732 or a portion of the network 732 may includea wireless or cellular network, and the coupling 724 may be a CodeDivision Multiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or another type of cellular or wirelesscoupling. In this example, the coupling 722 can implement any of avariety of types of data transfer technology, such as Single CarrierRadio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long range protocols, or otherdata transfer technology.

In example embodiments, the instructions 710 are transmitted or receivedover the network 732 using a transmission medium via a network interfacedevice (e.g., a network interface component included in thecommunication components 740) and utilizing any one of a number ofwell-known transfer protocols (e.g., Hypertext Transfer Protocol(HTTP)). Similarly, in other example embodiments, the instructions 710are transmitted or received using a transmission medium via the coupling722 (e.g., a peer-to-peer coupling) to the devices 720. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying the instructions 710for execution by the machine 700, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such software.

Furthermore, the machine-readable medium is non-transitory (in otherwords, not having any transitory signals) in that it does not embody apropagating signal. However, labeling the machine-readable medium“non-transitory” should not be construed to mean that the medium isincapable of movement: the medium should be considered as beingtransportable from one physical location to another. Additionally, sincethe machine-readable medium is tangible, the medium may be considered tobe a machine-readable device.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by one or more processors, travel information with a traveldate for a user; computing, by the one or more processors, asubscription value as a function of a booking date and the travel date;determining, by the one or more processors, a minimum travel value and amaximum purchase amount based on the computed subscription value;searching, by the one or more processors, a list of travel services thatare available on the travel date to identify candidate travel serviceseach having a first cost that exceeds the minimum travel value;selecting, by the one or more processors, a subset of the candidatetravel services that each have a second cost that is less than themaximum purchase amount; and generating, by the one or more processors,for display in a graphical user interface to the user, one or moreinteractive visual representations of the selected subset of thecandidate travel services.
 2. The computer-implemented method of claim1, wherein the travel information includes a geographical destinationand a length of stay; and wherein the travel date comprises a date ofarrival at the geographical destination.
 3. The computer-implementedmethod of claim 1, wherein: the booking date is a current date on whichthe searching step, the selecting step, and the generating step areperformed; and the subscription value comprises an accumulated valueportion and an amortized value portion.
 4. The computer-implementedmethod of claim 3, further comprising computing the accumulated valueportion of the subscription value by: determining a time intervalbetween the booking date and the travel date; and accumulating thesubscription value over the determined time interval.
 5. Thecomputer-implemented method of claim 4, wherein the time interval ismonthly, further comprising: determining a number of months between thebooking date and the travel date, wherein the subscription value iscomputed as a function of a monthly subscription cost to the user andthe number of months.
 6. The computer-implemented method of claim 3,further comprising computing the amortized value portion of thesubscription value by: determining an annual cost of a subscription ofthe user; dividing the annual cost by a specified repeated time intervalin a year; determining a number of times the time interval repeatsbetween the booking date and the travel date; and computing theamortized value as a function of the divided annual cost and thedetermined number of times.
 7. The computer-implemented method of claim6, wherein the specified repeated time interval is a week.
 8. Thecomputer-implemented method of claim 3, wherein the minimum travel valueis determined based on a percentage of the accumulated value portion. 9.The computer-implemented method of claim 3, wherein the maximum purchaseamount is determined as a function of the amortized value portion andthe accumulated value portion.
 10. The computer-implemented method ofclaim 9, wherein the maximum purchase amount is determined based on anadjusted average of the amortized value portion and the accumulatedvalue portion.
 11. The computer-implemented method of claim 10, whereinthe adjusted average is adjusted based on an expected margin value,wherein the expected margin value is positive or negative, and whereinthe expected margin value is computed based on at least one of a lengthof time between the booking date and the travel date or a type of travelservice.
 12. The computer-implemented method of claim 1, filtering theidentified candidate travel services based on a cancellation policy ofthe identified candidate travel services.
 13. The computer-implementedmethod of claim 1, wherein the travel services comprise at least one ofhotels, rental cars, airfares, homes/residences, experiential travel,guided tours, cruises, train fares, private aviation, bespoke travel,event-based travel, or space travel.
 14. The computer-implemented methodof claim 1, wherein the graphical user interface is presented via asubscription service associated with the subscription value, furthercomprising aggregating the list of travel services by accessing one ormore third-party databases that include the respective first costs ofthe travel services, wherein the travel services are available tonon-subscribers of the subscription service for purchase at therespective first costs.
 15. The computer-implemented method of claim 14,further comprising accessing one or more databases of the subscriptionservice to obtain the second cost for each of the list of travelservices, wherein the travel services are available to subscribers ofthe subscription service for selection to be reserved.
 16. Thecomputer-implemented method of claim 1, further comprising: receiving auser selection of a visual representation of the one or more visualrepresentations via the graphical user interface; reserving the travelservice for the user associated with the selected visual representation;and preventing the user from reserving additional travel services untilthe reserved travel service expires.
 17. The computer-implemented methodof claim 1, further comprising: applying a machine learning technique toassociate the user with a classification, wherein the machine learningtechnique is trained to establish a relationship between user travelactivity and user classification, wherein: selecting the subset of thecandidate travel services further comprises filtering the candidatetravel services based on the classification of the user.
 18. Thecomputer-implemented method of claim 17, wherein a first userclassification indicates user activity with a subscription service thatexceeds a threshold amount and a second user classification indicatesuser activity with the subscription service that is less than thethreshold amount, further comprising generating individualized travelservice lists for the user based on travel behaviors, geographicallocation, demographics, or a margin target for the user; and enablingthe user to interact with the graphical user interface to sort thetravel services based on a predicted likelihood that the user willreserve the travel services using a preference technique or arecommendation technique.
 19. A system comprising: a memory that storesinstructions; and one or more processors on a server configured by theinstructions to perform operations comprising: receiving travelinformation with a travel date for a user; computing a subscriptionvalue as a function of a booking date and the travel date; determining aminimum travel value and a maximum purchase amount based on the computedsubscription value; searching a list of travel services that areavailable on the travel date to identify candidate travel services eachhaving a first cost that exceeds the minimum travel value; selecting asubset of the candidate travel services that each have a second costthat is less than the maximum purchase amount; and generating fordisplay to a user, in a graphical user interface, one or moreinteractive visual representations of the selected subset of thecandidate travel services.
 20. A non-transitory computer-readable mediumcomprising instructions stored thereon that are executable by at leastone processor to cause a computing device to perform operationscomprising: receiving travel information with a travel date for a user;computing a subscription value as a function of a booking date and thetravel date; determining a minimum travel value and a maximum purchaseamount based on the computed subscription value; searching a list oftravel services that are available on the travel date to identifycandidate travel services each having a first cost that exceeds theminimum travel value; selecting a subset of the candidate travelservices that each have a second cost that is less than the maximumpurchase amount; and generating for display to a user, in a graphicaluser interface, one or more interactive visual representations of theselected subset of the candidate travel services.